Stimulating Unconventional Reservoirs: Lessons Learned, Successful Practices, Areas for Improvement
Bibliographic record
Abstract
Abstract The term "unconventional reservoir" has different meanings to different people. Certain reservoirs termed unconventional have a rock matrix consisting of inter-particle pore networks with very small pore connections imparting very poor fluid-flow characteristics. Abundant volumes of oil or gas can be stored in these rocks, and often the rock is high in organic content and the source of the hydrocarbon. Yet because of marginal rock matrix quality, these reservoirs generally require both natural and induced fracture networks to enable economic recovery of the hydrocarbon. Rock types in this class include shale and coalbed methane (CBM.) The term shale is a catchall for any rock consisting of extremely small framework particles with minute pores charged with hydrocarbon and includes carbonate and quartz-rich rocks. Another type of unconventional reservoir is stacked pay units exhibiting somewhat better pore characteristics than in the case outlined above but with the individual units tending to be lenticular in shape and having an extremely small size or volume. These two classes of unconventional reservoirs are amenable to well stimulation and will be the focus of this paper. The above rock types when commercially exploited are known as resource plays. Once a low-priority, the depletion of conventional reservoirs and improving price for oil and gas has driven unconventional reservoirs to an important place in the oil and gas industry. In some regions (i.e., Rocky Mountain province), unconventional reservoirs represent the primary target of current activity and remaining hydrocarbon development. Given their unique petrophysical properties, each type of unconventional reservoir requires a unique approach to well stimulation, with often differing objectives than exist with conventional reservoir types. This paper reviews the characteristics of the basic unconventional reservoir types, lessons learned and successful stimulation practices developed in completing these reservoirs, and areas for improvement in treatment and reservoir characterization and treatment design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".